Journal Article
Research Support, Non-U.S. Gov't
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Expression patterns of USP22 and potential targets BMI-1, PTEN, p-AKT in non-small-cell lung cancer.

BACKGROUND: Recent researches document that an oncogenic role of USP22 activation may contribute to progression and predict the prognosis. We have reported that USP22 mediates cell survival and proliferation by promoting the expression of BMI-1 and upregulation of activated AKT pathway in colon cancer cells. However, little is known about its mechanisms in non-small-cell lung cancer (NSCLC). Here the authors investigated the significance of activation of USP22 and potential targets BMI-1, PTEN and phospho-AKT (p-AKT) in NSCLC.

METHODS: Expression levels of USP22, BMI-1, PTEN and p-AKT in samples from 114 patients with NSCLC were evaluated immunohistochemically using the tissue microarray method. Clinical significance was analyzed by multivariate Cox regression analysis, Kaplan-Meier curves and the log-rank test.

RESULTS: Immunohistochemically, USP22, BMI-1, p-AKT and PTEN were positive in 66.66%, 78.07%, 71.92% and 43.85% of NSCLC samples, respectively. Statistical correlation analysis showed USP22 to be significantly correlated with BMI-1 (r=0.315, P=0.001), p-AKT (r=0.271, P=0.003), and PTEN (r=-0.384, P<0.0001). NSCLCs with positive expression of USP22, BMI-1, p-AKT, and negative expression of PTEN were significantly correlated to tumor size (P=0.0240), differentiation (P=0.0457), pT classification (P=0.0077), pN classification (P=0.0064), and AJCC stage (P=0.0363) and poor overall survival (P<0.001). Multivariate Cox proportional hazards model analysis showed that the combined 4 markers was the independent prognostic indicator of overall survival (P<0.001; HR, 5.974; 95% CI, 3.307-10.791).

CONCLUSIONS: The simultaneous targeting of USP22, and its downstream signal transduction molecules seem highly informative in stratification of the cancer into subgroups with distinct likelihood of therapy failure, which contribute to make decision process regarding the individualized therapy selection and optimization.

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